{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 复习上课内容以及复现课程代码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在本部分，你需要复习上课内容和课程代码后，自己复现课程代码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Node:\n",
    "    # 该类为所有其他图节点类的父类\n",
    "    def __init__(self, inputs=[]):\n",
    "        #定义每个节点的输入和输出\n",
    "        self.inputs = inputs\n",
    "        self.outputs = []\n",
    "        \n",
    "        #每个节点都是其输入节点的输出节点\n",
    "        for n in self.inputs:\n",
    "            n.outputs.append(self)\n",
    "            # set 'self' node as inbound_nodes's outbound_nodes\n",
    "\n",
    "        self.value = None\n",
    "        \n",
    "        # 每个结点需要保存梯度\n",
    "        self.gradients = {}\n",
    "        # keys are the inputs to this node, and their\n",
    "        # values are the partials of this node with \n",
    "        # respect to that input.\n",
    "        # \\partial{node}{input_i}\n",
    "        \n",
    "\n",
    "    def forward(self):\n",
    "        #前向传播函数 继承该类的其他类会覆写该函数\n",
    "        '''\n",
    "        Forward propagation. \n",
    "        Compute the output value vased on 'inbound_nodes' and store the \n",
    "        result in self.value\n",
    "        '''\n",
    "        raise NotImplemented\n",
    "    \n",
    "\n",
    "    def backward(self):\n",
    "        #反向传播函数，继承该类的其他类会覆写该函数\n",
    "\n",
    "        raise NotImplemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Input(Node):\n",
    "    # 输入节点，包括神经网络输入节点，权重节点，和偏差节点\n",
    "    def __init__(self):\n",
    "        '''\n",
    "        An Input node has no inbound nodes.\n",
    "        So no need to pass anything to the Node instantiator.\n",
    "        '''\n",
    "        Node.__init__(self)\n",
    "\n",
    "    def forward(self, value=None):\n",
    "        '''\n",
    "        Only input node is the node where the value may be passed\n",
    "        as an argument to forward().\n",
    "        All other node implementations should get the value of the \n",
    "        previous node from self.inbound_nodes\n",
    "        \n",
    "        Example: \n",
    "        val0: self.inbound_nodes[0].value\n",
    "        '''\n",
    "        #定义节点数值\n",
    "        if value is not None:\n",
    "            self.value = value\n",
    "            ## It's is input node, when need to forward, this node initiate self's value.\n",
    "\n",
    "        # Input subclass just holds a value, such as a data feature or a model parameter(weight/bias)\n",
    "               \n",
    "    def backward(self):\n",
    "        #计算节点梯度\n",
    "        self.gradients = {self:0} # initialization \n",
    "        for n in self.outputs:\n",
    "            #以下计算该节点的输出节点对该节点的梯度\n",
    "            grad_cost = n.gradients[self]\n",
    "            self.gradients[self] = grad_cost * 1\n",
    "            \n",
    "        \n",
    "        # input N --> N1, N2\n",
    "        # \\partial L / \\partial N \n",
    "        # ==> \\partial L / \\partial N1 * \\ partial N1 / \\partial N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 基本用不到，作用是直接的sum求和\n",
    "class Add(Node):\n",
    "    def __init__(self, *nodes):\n",
    "        Node.__init__(self, nodes)\n",
    "\n",
    "\n",
    "    def forward(self):\n",
    "        self.value = sum(map(lambda n: n.value, self.inputs))\n",
    "        ## when execute forward, this node caculate value as defined."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Linear(Node):\n",
    "    #全连接网络层的计算\n",
    "    def __init__(self, nodes, weights, bias):\n",
    "        Node.__init__(self, [nodes, weights, bias])\n",
    "\n",
    "    def forward(self):\n",
    "        #前向传播计算 y = w*x + b\n",
    "        inputs = self.inputs[0].value\n",
    "        weights = self.inputs[1].value\n",
    "        bias = self.inputs[2].value\n",
    "\n",
    "        self.value = np.dot(inputs, weights) + bias\n",
    "        \n",
    "    def backward(self):\n",
    "        #反向传播计算\n",
    "        # initial a partial for each of the inbound_nodes.\n",
    "        self.gradients = {n: np.zeros_like(n.value) for n in self.inputs}\n",
    "\n",
    "        for n in self.outputs:\n",
    "            # Get the partial of the cost w.r.t this node.\n",
    "            grad_cost = n.gradients[self]\n",
    "            #以下分别计算对inputs， weights, bias的梯度\n",
    "            self.gradients[self.inputs[0]] = np.dot(grad_cost, self.inputs[1].value.T)\n",
    "            self.gradients[self.inputs[1]] = np.dot(self.inputs[0].value.T, grad_cost)\n",
    "            self.gradients[self.inputs[2]] = np.sum(grad_cost, axis=0, keepdims=False)\n",
    "\n",
    "        # WX + B / W ==> X\n",
    "        # WX + B / X ==> W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sigmoid(Node):\n",
    "    #定义sigmoid函数\n",
    "    def __init__(self, node):\n",
    "        Node.__init__(self, [node])\n",
    "\n",
    "\n",
    "    def _sigmoid(self, x):\n",
    "        return 1./(1 + np.exp(-1 * x))\n",
    "\n",
    "    def forward(self):\n",
    "        #前向 即为sigmoid函数计算\n",
    "        self.x = self.inputs[0].value   # [0] input is a list\n",
    "        self.value = self._sigmoid(self.x)\n",
    "\n",
    "    def backward(self):\n",
    "        #反向传播计算梯度\n",
    "        self.partial = self._sigmoid(self.x) * (1 - self._sigmoid(self.x))\n",
    "        \n",
    "        # y = 1 / (1 + e^-x)\n",
    "        # y' = 1 / (1 + e^-x) (1 - 1 / (1 + e^-x))\n",
    "        \n",
    "        self.gradients = {n: np.zeros_like(n.value) for n in self.inputs}\n",
    "        \n",
    "        for n in self.outputs:\n",
    "            grad_cost = n.gradients[self]  # Get the partial of the cost with respect to this node.\n",
    "\n",
    "            self.gradients[self.inputs[0]] = grad_cost * self.partial\n",
    "            # use * to keep all the dimension same!."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MSE(Node):\n",
    "    # 定义平均平方误差\n",
    "    def __init__(self, y, a):\n",
    "        Node.__init__(self, [y, a])\n",
    "\n",
    "\n",
    "    def forward(self):\n",
    "        #前向传播计算\n",
    "        y = self.inputs[0].value.reshape(-1, 1)\n",
    "        a = self.inputs[1].value.reshape(-1, 1)\n",
    "        assert(y.shape == a.shape)\n",
    "\n",
    "        self.m = self.inputs[0].value.shape[0]\n",
    "        self.diff = y - a\n",
    "\n",
    "        self.value = np.mean(self.diff**2)\n",
    "\n",
    "\n",
    "    def backward(self):\n",
    "        #反向计算相应梯度\n",
    "        self.gradients[self.inputs[0]] = (2 / self.m) * self.diff\n",
    "        self.gradients[self.inputs[1]] = (-2 / self.m) * self.diff\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forward_and_backward(outputnode, graph):\n",
    "    # execute all the forward method of sorted_nodes.\n",
    "\n",
    "    ## In practice, it's common to feed in mutiple data example in each forward pass rather than just 1. Because the examples can be processed in parallel. The number of examples is called batch size.\n",
    "    for n in graph:\n",
    "        n.forward()\n",
    "        ## each node execute forward, get self.value based on the topological sort result.\n",
    "\n",
    "    for n in graph[::-1]:\n",
    "        n.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 拓扑排序，确保所有点都已经执行完毕\n",
    "def topological_sort(feed_dict):\n",
    "    input_nodes = [n for n in feed_dict.keys()]\n",
    "    G = {}\n",
    "    nodes = [n for n in input_nodes]\n",
    "    while len(nodes) > 0:\n",
    "        n = nodes.pop(0)\n",
    "        if n not in G:\n",
    "            G[n] = {'in': set(), 'out': set()}\n",
    "        for m in n.outputs:\n",
    "            if m not in G:\n",
    "                G[m] = {'in':set(), 'out': set()}\n",
    "            G[n]['out'].add(m)\n",
    "            G[m]['in'].add(n)\n",
    "            nodes.append(m)\n",
    "    L = []\n",
    "    S = set(input_nodes)\n",
    "    while len(S) > 0:\n",
    "        n = S.pop()\n",
    "        if isinstance(n, Input):\n",
    "            n.value = feed_dict[n]\n",
    "        L.append(n)\n",
    "        for m in n.outputs:\n",
    "            G[n]['out'].remove(m)\n",
    "            G[m]['in'].remove(n)\n",
    "            if len(G[m]['in']) == 0:\n",
    "                S.add(m)\n",
    "    return L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sgd_update(trainables, learning_rate=1e-2):\n",
    "    for t in trainables:\n",
    "        t.value += -1 * learning_rate * t.gradients[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "        4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "        9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "        4.0300e+00],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        5.6400e+00],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "        6.4800e+00],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        7.8800e+00]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "losses = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s1 <__main__.Sigmoid object at 0x1a3c2eb9e8>\n",
      "graph [<__main__.Input object at 0x1a3c2eb898>, <__main__.Input object at 0x1a3c2eb8d0>, <__main__.Input object at 0x1a3c2eb908>, <__main__.Input object at 0x1a3c297b38>, <__main__.Input object at 0x1a3c29f748>, <__main__.Input object at 0x1a3c2eb160>, <__main__.Linear object at 0x1a3c2eb978>, <__main__.Sigmoid object at 0x1a3c2eb9e8>, <__main__.Linear object at 0x1a3c2eb9b0>, <__main__.MSE object at 0x1a3c2eba20>]\n",
      "Total number of examples = 506\n",
      "Epoch: 1, Loss: 202.206\n",
      "Epoch: 101, Loss: 6.912\n",
      "Epoch: 201, Loss: 6.419\n",
      "Epoch: 301, Loss: 5.336\n",
      "Epoch: 401, Loss: 4.507\n",
      "Epoch: 501, Loss: 4.387\n",
      "Epoch: 601, Loss: 4.736\n",
      "Epoch: 701, Loss: 3.779\n",
      "Epoch: 801, Loss: 4.325\n",
      "Epoch: 901, Loss: 4.441\n",
      "Epoch: 1001, Loss: 3.879\n",
      "Epoch: 1101, Loss: 3.982\n",
      "Epoch: 1201, Loss: 3.981\n",
      "Epoch: 1301, Loss: 4.297\n",
      "Epoch: 1401, Loss: 3.789\n",
      "Epoch: 1501, Loss: 4.060\n",
      "Epoch: 1601, Loss: 3.475\n",
      "Epoch: 1701, Loss: 3.576\n",
      "Epoch: 1801, Loss: 3.927\n",
      "Epoch: 1901, Loss: 4.146\n",
      "Epoch: 2001, Loss: 3.208\n",
      "Epoch: 2101, Loss: 3.726\n",
      "Epoch: 2201, Loss: 3.846\n",
      "Epoch: 2301, Loss: 3.768\n",
      "Epoch: 2401, Loss: 3.830\n",
      "Epoch: 2501, Loss: 3.405\n",
      "Epoch: 2601, Loss: 3.688\n",
      "Epoch: 2701, Loss: 3.784\n",
      "Epoch: 2801, Loss: 4.074\n",
      "Epoch: 2901, Loss: 3.038\n",
      "Epoch: 3001, Loss: 2.795\n",
      "Epoch: 3101, Loss: 3.854\n",
      "Epoch: 3201, Loss: 3.037\n",
      "Epoch: 3301, Loss: 3.331\n",
      "Epoch: 3401, Loss: 3.625\n",
      "Epoch: 3501, Loss: 3.709\n",
      "Epoch: 3601, Loss: 3.614\n",
      "Epoch: 3701, Loss: 3.651\n",
      "Epoch: 3801, Loss: 3.709\n",
      "Epoch: 3901, Loss: 3.908\n",
      "Epoch: 4001, Loss: 3.544\n",
      "Epoch: 4101, Loss: 3.142\n",
      "Epoch: 4201, Loss: 3.380\n",
      "Epoch: 4301, Loss: 3.550\n",
      "Epoch: 4401, Loss: 3.747\n",
      "Epoch: 4501, Loss: 3.289\n",
      "Epoch: 4601, Loss: 3.242\n",
      "Epoch: 4701, Loss: 3.359\n",
      "Epoch: 4801, Loss: 2.929\n",
      "Epoch: 4901, Loss: 2.998\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.utils import shuffle, resample\n",
    "#from miniflow import *\n",
    "\n",
    "# Load data\n",
    "data = load_boston()\n",
    "# x_:输入数据， y_:标签数据\n",
    "X_ = data['data']\n",
    "y_ = data['target']\n",
    "\"\"\"\n",
    "np.mean(X_, axis=0):\n",
    "计算X_,列的均值\n",
    "np.std(X_, axis=0):\n",
    "计算每一列的标准差\n",
    "\"\"\"\n",
    "# Normalize data 标准化数据\n",
    "X_ = (X_ - np.mean(X_, axis=0)) / np.std(X_, axis=0)\n",
    "# n_features = X_.shape[1]: 第一维的长度\n",
    "n_features = X_.shape[1]\n",
    "n_hidden = 10\n",
    "\"\"\"\n",
    "numpy.random.randn(d0,d1,…,dn)\n",
    "randn函数返回一个或一组样本，具有标准正态分布。\n",
    "dn表格每个维度\n",
    "返回值为指定维度的array\n",
    "\"\"\"\n",
    "W1_ = np.random.randn(n_features, n_hidden)\n",
    "b1_ = np.zeros(n_hidden)\n",
    "W2_ = np.random.randn(n_hidden, 1)\n",
    "b2_ = np.zeros(1)\n",
    "\n",
    "# Neural network\n",
    "X, y = Input(), Input()\n",
    "W1, b1 = Input(), Input()\n",
    "W2, b2 = Input(), Input()\n",
    "\n",
    "l1 = Linear(X, W1, b1) # wT*x+b \n",
    "s1 = Sigmoid(l1)\n",
    "print(\"s1\", s1)\n",
    "l2 = Linear(s1, W2, b2) # WTS1+b\n",
    "cost = MSE(y, l2) #l2:预测值，y:真实值\n",
    "\n",
    "feed_dict = {\n",
    "    X: X_,\n",
    "    y: y_,\n",
    "    W1: W1_,\n",
    "    b1: b1_,\n",
    "    W2: W2_,\n",
    "    b2: b2_\n",
    "}\n",
    "\n",
    "epochs = 5000\n",
    "# Total number of examples\n",
    "m = X_.shape[0]\n",
    "batch_size = 16 \n",
    "steps_per_epoch = m // batch_size\n",
    "\n",
    "# 拓扑排序，按照入度为0的先算\n",
    "graph = topological_sort(feed_dict)\n",
    "print(\"graph\", graph)\n",
    "trainables = [W1, b1, W2, b2]\n",
    "\n",
    "print(\"Total number of examples = {}\".format(m))\n",
    "\n",
    "# Step 4\n",
    "for i in range(epochs):\n",
    "    loss = 0\n",
    "    for j in range(steps_per_epoch):\n",
    "        # Step 1\n",
    "        # Randomly sample a batch of examples 随机取一些样本\n",
    "        X_batch, y_batch = resample(X_, y_, n_samples=batch_size)\n",
    "\n",
    "        # Reset value of X and y Inputs\n",
    "        X.value = X_batch\n",
    "        y.value = y_batch\n",
    "\n",
    "        # Step 2\n",
    "        _ = None\n",
    "        forward_and_backward(_, graph) # set output node not important.\n",
    "\n",
    "        # Step 3\n",
    "        rate = 1e-2\n",
    "    \n",
    "        sgd_update(trainables, rate)\n",
    "\n",
    "        loss += graph[-1].value\n",
    "    \n",
    "    if i % 100 == 0: \n",
    "        print(\"Epoch: {}, Loss: {:.3f}\".format(i+1, loss/steps_per_epoch))\n",
    "        losses.append(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 重写一个单独的forward，进行预测\n",
    "def forward(outputNode,graph):\n",
    "    for n in graph:\n",
    "        n.forward()\n",
    "    return outputNode.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19.91343784],\n",
       "       [28.15001676],\n",
       "       [27.16642841],\n",
       "       [24.03900084],\n",
       "       [13.72681785],\n",
       "       [34.96495762],\n",
       "       [14.44105743],\n",
       "       [12.45257139],\n",
       "       [17.9394075 ],\n",
       "       [51.82792265],\n",
       "       [13.77552878],\n",
       "       [14.16392985],\n",
       "       [28.15001676],\n",
       "       [16.37014317],\n",
       "       [23.06338332],\n",
       "       [18.43910766]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forward(l2, graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a3ce68518>]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(losses)), losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.13222947],\n",
       "       [ 6.62729148],\n",
       "       [10.88142172],\n",
       "       [ 7.03981609],\n",
       "       [ 9.49238229],\n",
       "       [ 7.85870925],\n",
       "       [ 5.17713806],\n",
       "       [ 5.20505656],\n",
       "       [ 9.03105159],\n",
       "       [11.07377852]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W2.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ = data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.320e-03, 1.800e+01, 2.310e+00, 0.000e+00, 5.380e-01, 6.575e+00,\n",
       "       6.520e+01, 4.090e+00, 1.000e+00, 2.960e+02, 1.530e+01, 3.969e+02,\n",
       "       4.980e+00])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting keras\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl (377kB)\n",
      "\u001b[K    100% |████████████████████████████████| 378kB 4.2kB/s ta 0:00:02\n",
      "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (1.1.0)\n",
      "Requirement already satisfied: scipy>=0.14 in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (1.2.1)\n",
      "Requirement already satisfied: six>=1.9.0 in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (1.12.0)\n",
      "Requirement already satisfied: numpy>=1.9.1 in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (1.16.2)\n",
      "Requirement already satisfied: h5py in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (2.9.0)\n",
      "Requirement already satisfied: pyyaml in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (5.1)\n",
      "Requirement already satisfied: keras-applications>=1.0.6 in /Users/stone/anaconda3/lib/python3.7/site-packages (from keras) (1.0.8)\n",
      "Installing collected packages: keras\n",
      "Successfully installed keras-2.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense\n",
    "from keras.models import Sequential\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(units=64, activation='sigmoid', input_dim=13))\n",
    "model.add(Dense(units=30, activation='sigmoid', input_dim=64))\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "model.compile(loss='mse',\n",
    "             optimizer='sgd',\n",
    "             metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/stone/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Epoch 1/500\n"
     ]
    }
   ],
   "source": [
    "model.fit(x_, y_, epochs=500, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 回答一下理论题目"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. What does a neuron compute?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 实质上是各种矩阵变换操作\n",
    "- 给定输入数据，根据预先定义的模型架构，函数等，通过反向传播不断更新参数值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  2. Why we use non-linear activation funcitons in neural networks?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 如果使用线性函数，经过矩阵操作后，得出的，还会是一个线性的结果，体现不出隐藏层的作用，也无法学习和模拟复杂的现实中的一些问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. What is the 'Logistic Loss' ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ g(z) = \\frac{1}{1+e^{-z}} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Assume that you are building a binary classifier for detecting if an image containing cats, which activation functions would you recommen using for the output layer ?\n",
    "\n",
    "A. ReLU    \n",
    "B. Leaky ReLU    \n",
    "C. sigmoid    \n",
    "D. tanh  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 答案：A C\n",
    "- 理由：因为问题是一个二分类问题，A C两个激活函数的范围都是（0，1）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Why we don't use zero initialization for all parameters ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 如果使用0进行初始化，最终结果也是0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Can you implement the softmax function using python ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ softmax(x_i) = \\frac{exp(x_i)}{\\sum_i{exp(x_i)}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# axis=0, 普通的相加\n",
    "def softmax(x):\n",
    "    return np.exp(x)/np.sum(np.exp(x), axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8360188 , 0.11314284, 0.05083836])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "softmax([3.0, 1.0, 0.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.实践题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In this practical part, you will build a simple digits recognizer to check if the digit in the image is larger than 5. This assignmnet will guide you step by step to finish your first small project in this course ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1 - Packages  \n",
    "sklearn is a famous package for machine learning.   \n",
    "matplotlib is a common package for vasualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 - Overvie of the dataset  \n",
    "    - a training set has m_train images labeled as 0 if the digit < 5 or 1 if the digit >= 5\n",
    "    - a test set contains m_test images labels as if the digit < 5 or 1 if the digit >= 5\n",
    "    - each image if of shape (num_px, num_px ). Thus, each image is square(height=num_px and  width = num_px)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the data \n",
    "digits = datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Vilizating the data\n",
    "for i in range(1,11):\n",
    "    plt.subplot(2,5,i)\n",
    "    plt.imshow(digits.data[i-1].reshape([8,8]),cmap=plt.cm.gray_r)\n",
    "    plt.text(3,10,str(digits.target[i-1]))\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training set and test set \n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reformulate the label. \n",
    "# If the digit is smaller than 5, the label is 0.\n",
    "# If the digit is larger than 5, the label is 1.\n",
    "\n",
    "y_train[y_train < 5 ] = 0\n",
    "y_train[y_train >= 5] = 1\n",
    "y_test[y_test < 5] = 0\n",
    "y_test[y_test >= 5] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  3., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  9., ...,  7.,  0.,  0.],\n",
       "       [ 0.,  0.,  6., ...,  1.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  0.,  1., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ..., 16., 12.,  1.]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  3., 15., 16., 15.,  1.,  0.,  0.,  0.,  9., 11.,  9.,\n",
       "       16.,  3.,  0.,  0.,  0.,  1.,  0.,  3., 16.,  3.,  0.,  0.,  0.,\n",
       "        0.,  0.,  9., 14.,  0.,  0.,  0.,  0.,  4., 15., 15., 16.,  6.,\n",
       "        0.,  0.,  0.,  2., 12., 15.,  7.,  1.,  0.,  0.,  0.,  0., 13.,\n",
       "        8.,  0.,  0.,  0.,  0.,  0.,  4., 14.,  1.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  9., 13.,  6.,  0.,  0.,  0.,  0.,  0., 14.,  7., 11.,\n",
       "        3.,  0.,  0.,  0.,  4.,  7.,  8.,  5.,  8.,  0.,  0.,  0.,  8.,\n",
       "       10., 15., 14.,  9.,  0.,  0.,  0.,  0.,  4.,  7.,  9., 13.,  1.,\n",
       "        0.,  0.,  0.,  0.,  0.,  0.,  5., 11.,  0.,  0.,  0.,  2.,  0.,\n",
       "        2., 12.,  6.,  0.,  0.,  0., 10., 14., 14.,  7.,  0.,  0.])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 0, 1])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1347,)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(y_train.T).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1347, 1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.reshape(-1, 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1347, 64)\n",
      "(450, 64)\n",
      "(1347,)\n",
      "(450,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3- Architecture of the neural network"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Mathematical expression of the algorithm:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For one example $x^{(i)}$:   \n",
    " $$ z^{(i)} = w^T * x^{(i)} +b $$   \n",
    " $$ y^{(i)} = a^{(i)} = sigmoid(z^{(i)})$$   \n",
    " $$L(a^{(i)},y^{(i)}) = -y^{(i)} log(a^{(i)})-(1-y^{(i)})log(1-a^{(i)})$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The total cost over all training examples:\n",
    "$$ J = \\frac{1}{m}\\sum_{i=1}^{m}L(a^{(i)},y^{(i)}) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4 - Building the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.1- Activation function    \n",
    "###### Exercise:\n",
    "Finish the sigmoid funciton "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    '''\n",
    "    Compute the sigmoid of z\n",
    "    Arguments: z -- a scalar or numpy array of any size.\n",
    "    \n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    '''\n",
    "    s = 1. / (1 + np.exp(-1 * z))\n",
    "    \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sigmoid([0,2]) = [0.5        0.88079708]\n"
     ]
    }
   ],
   "source": [
    "# Test your code \n",
    "# The result should be [0.5 0.88079708]\n",
    "print(\"sigmoid([0,2]) = \" + str(sigmoid(np.array([0,2]))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.1-Initializaing parameters\n",
    "###### Exercise:\n",
    "Finishe the initialize_parameters function below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random innitialize the parameters\n",
    "\n",
    "def initialize_parameters(dim):\n",
    "    '''\n",
    "    Argument: dim -- size of the w vector    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim,1)\n",
    "    b -- initializaed scalar\n",
    "    '''\n",
    "    \n",
    "    w = np.random.randn(dim, 1)\n",
    "    b = 0.0\n",
    "#     print(b)\n",
    "    # assert作用:用于判断一个表达式，在表达式条件为 false 的时候触发异常\n",
    "    assert(w.shape == (dim,1))\n",
    "    assert(isinstance(b,float) or isinstance(b,int))\n",
    "    \n",
    "    return w,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-4.28001937e-01],\n",
       "        [-9.75320352e-01],\n",
       "        [ 9.53242716e-01],\n",
       "        [ 2.66809327e-01],\n",
       "        [-1.97303168e+00],\n",
       "        [-1.37778426e+00],\n",
       "        [ 1.30933673e-03],\n",
       "        [-3.77133610e-01],\n",
       "        [-9.03441828e-01],\n",
       "        [-1.32003570e-01]]), 0.0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initialize_parameters(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3-Forward and backward propagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Some mathematical expressions\n",
    "Forward Propagation:   \n",
    ". X    \n",
    ". A = $\\sigma(w^T*X+b) = (a^{(1)},a^{(2)},...,a^{(m)}$   \n",
    ". J = $-\\frac{1}{m} \\sum_{i=1}^{m}y^{(i)}log(a^{(i)}+(1-y^{(i)})log(1-a^{(i)})$       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some derivative: \n",
    "$$\\frac{\\partial{J}}{\\partial{w}} = \\frac{1}{m}X*(A-Y)^T$$   \n",
    "$$\\frac{\\partial{J}}{\\partial{b}} = \\frac{1}{m}\\sum_{i=1}^m(a^{(i)}-y^{(i)}) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Exercise:\n",
    "Finish the function below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot([1, 2], [[1], [2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def propagate(w,b,X,Y):\n",
    "    '''\n",
    "    Implement the cost function and its gradient for the propagation\n",
    "    \n",
    "    Arguments:\n",
    "    w - weights\n",
    "    b - bias\n",
    "    X - data\n",
    "    Y - ground truth\n",
    "    '''\n",
    "#     X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n",
    "    X = np.nan_to_num(X / X.max(axis=0))\n",
    "#     print(\"X\", X)\n",
    "    m = X.shape[1] # 列数\n",
    "    A = sigmoid(np.dot(X, w) + b)\n",
    "#     print(\"A.shape\", A.shape) # A.shape (1347, 1)\n",
    "#     print(\"A\", A)\n",
    "#     print(Y.T.shape) # (1, 1347)\n",
    "#     cost = (-1 / m)* np.dot(np.nan_to_num(Y*np.log(A))+(1-Y)*np.log(1-A)) \n",
    "    cost = (-1.0 / m)* (np.dot(Y.T, np.log(A)) + np.dot(1-Y.T, np.log(1-A)))\n",
    "#     print(\"cost\", cost)\n",
    "    \n",
    "    dw = (1.0/m)*np.dot(X.T, (A-Y))\n",
    "    db = (1.0/m)*np.sum(A-Y)\n",
    "    \n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    # 利用squeeze（）函数将表示向量的数组转换为秩为1的数组\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {'dw':dw,\n",
    "             'db':db}\n",
    "    return grads, cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "w, b = initialize_parameters(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  if sys.path[0] == '':\n",
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in true_divide\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'dw': array([[ 0.00000000e+00],\n",
       "         [ 1.14514469e-01],\n",
       "         [ 7.67677428e-01],\n",
       "         [ 2.93727153e+00],\n",
       "         [ 2.18499742e+00],\n",
       "         [-6.55874381e-01],\n",
       "         [-6.07987512e-01],\n",
       "         [-6.31133868e-02],\n",
       "         [ 9.74071554e-03],\n",
       "         [ 3.67611103e-01],\n",
       "         [ 1.58477613e+00],\n",
       "         [ 3.45921728e+00],\n",
       "         [ 3.29737645e+00],\n",
       "         [ 1.38501857e+00],\n",
       "         [-2.57147799e-01],\n",
       "         [-7.09490052e-02],\n",
       "         [ 1.34352626e-02],\n",
       "         [ 3.41203889e-01],\n",
       "         [ 1.65673922e+00],\n",
       "         [ 2.24278403e+00],\n",
       "         [ 2.42984876e+00],\n",
       "         [ 2.04438459e+00],\n",
       "         [ 3.86209291e-01],\n",
       "         [-4.21620743e-03],\n",
       "         [ 1.34104119e-02],\n",
       "         [ 6.19703999e-01],\n",
       "         [ 1.61817160e+00],\n",
       "         [ 6.42343461e-01],\n",
       "         [ 1.16538534e+00],\n",
       "         [ 1.17013956e+00],\n",
       "         [ 9.23713551e-01],\n",
       "         [ 1.71302563e-02],\n",
       "         [ 0.00000000e+00],\n",
       "         [ 1.55924064e+00],\n",
       "         [ 2.57026426e+00],\n",
       "         [ 2.27075180e+00],\n",
       "         [ 2.19980552e+00],\n",
       "         [ 1.01084359e+00],\n",
       "         [ 8.68352955e-01],\n",
       "         [ 0.00000000e+00],\n",
       "         [ 5.22094520e-02],\n",
       "         [ 1.20863925e+00],\n",
       "         [ 4.00581737e+00],\n",
       "         [ 3.75763201e+00],\n",
       "         [ 2.55231817e+00],\n",
       "         [ 1.35240410e+00],\n",
       "         [ 3.22331501e-01],\n",
       "         [-2.97487993e-03],\n",
       "         [ 2.57784838e-02],\n",
       "         [ 3.91587341e-01],\n",
       "         [ 3.23293520e+00],\n",
       "         [ 3.89277611e+00],\n",
       "         [ 3.54034197e+00],\n",
       "         [ 2.31853472e+00],\n",
       "         [ 6.61458512e-01],\n",
       "         [ 1.21512418e-01],\n",
       "         [ 1.55391436e-02],\n",
       "         [ 1.07607179e-01],\n",
       "         [ 7.72410193e-01],\n",
       "         [ 2.72205968e+00],\n",
       "         [ 3.41700477e+00],\n",
       "         [ 1.78264087e+00],\n",
       "         [ 7.99713267e-01],\n",
       "         [ 2.20972929e-01]]), 'db': 3.8266521852134043}, array(33.77565557))"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "propagate(w,b, X_train, y_train.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.4 -Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Exercise:\n",
    "Minimizing the cost function using gradient descent.   \n",
    "$$\\theta = \\theta - \\alpha*d\\theta$$ where $\\alpha$ is the learning rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost=False):\n",
    "    '''\n",
    "    This function optimize w and b by running a gradient descen algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w - weights\n",
    "    b - bias\n",
    "    X - data\n",
    "    Y - ground truth\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params - dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    costs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        \n",
    "        dw = grads['dw']\n",
    "        db = grads['db']\n",
    "        \n",
    "         \n",
    "        w = w - learning_rate*dw\n",
    "        b = b - learning_rate*db\n",
    "        \n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\":w,\n",
    "              \"b\":b}\n",
    "    \n",
    "    grads = {\"dw\":dw,\n",
    "             \"db\":db}\n",
    "    \n",
    "    return params, grads, costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "w, b = initialize_parameters(64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: RuntimeWarning: divide by zero encountered in true_divide\n",
      "  if sys.path[0] == '':\n",
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:12: RuntimeWarning: invalid value encountered in true_divide\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 30.226680\n",
      "Cost after iteration 100: 10.363022\n",
      "Cost after iteration 200: 8.778219\n",
      "Cost after iteration 300: 7.978161\n",
      "Cost after iteration 400: 7.477927\n",
      "Cost after iteration 500: 7.132824\n",
      "Cost after iteration 600: 6.879915\n",
      "Cost after iteration 700: 6.686460\n",
      "Cost after iteration 800: 6.533576\n",
      "Cost after iteration 900: 6.409607\n",
      "Cost after iteration 1000: 6.306980\n",
      "Cost after iteration 1100: 6.220572\n",
      "Cost after iteration 1200: 6.146798\n",
      "Cost after iteration 1300: 6.083072\n",
      "Cost after iteration 1400: 6.027480\n",
      "Cost after iteration 1500: 5.978574\n",
      "Cost after iteration 1600: 5.935235\n",
      "Cost after iteration 1700: 5.896585\n",
      "Cost after iteration 1800: 5.861922\n",
      "Cost after iteration 1900: 5.830677\n",
      "Cost after iteration 2000: 5.802387\n",
      "Cost after iteration 2100: 5.776667\n",
      "Cost after iteration 2200: 5.753195\n",
      "Cost after iteration 2300: 5.731702\n",
      "Cost after iteration 2400: 5.711956\n",
      "Cost after iteration 2500: 5.693762\n",
      "Cost after iteration 2600: 5.676952\n",
      "Cost after iteration 2700: 5.661378\n",
      "Cost after iteration 2800: 5.646915\n",
      "Cost after iteration 2900: 5.633452\n",
      "({'w': array([[ 1.08214575e+00],\n",
      "       [-2.67279968e+00],\n",
      "       [ 8.86903071e-01],\n",
      "       [-7.12827793e-01],\n",
      "       [ 1.11786041e+00],\n",
      "       [ 1.23476945e+00],\n",
      "       [ 2.33791823e+00],\n",
      "       [-6.79497372e-01],\n",
      "       [ 2.16144638e-01],\n",
      "       [-2.19925395e-01],\n",
      "       [ 1.07022177e+00],\n",
      "       [ 9.22555978e-01],\n",
      "       [-1.88358589e-01],\n",
      "       [-5.63343363e-01],\n",
      "       [ 5.04072468e-01],\n",
      "       [-1.26028341e+00],\n",
      "       [ 3.27972944e-01],\n",
      "       [-4.60005288e-02],\n",
      "       [ 2.50028298e+00],\n",
      "       [-3.13420402e-01],\n",
      "       [-3.02814508e+00],\n",
      "       [-7.64693033e-01],\n",
      "       [-2.03473210e+00],\n",
      "       [ 1.43594280e-01],\n",
      "       [-1.07758870e+00],\n",
      "       [-1.98829562e+00],\n",
      "       [ 5.66649248e-01],\n",
      "       [ 2.44188272e+00],\n",
      "       [ 3.18504272e-01],\n",
      "       [ 1.97346774e+00],\n",
      "       [-1.51523245e+00],\n",
      "       [-4.84434583e-01],\n",
      "       [-8.59493024e-01],\n",
      "       [-2.77606386e+00],\n",
      "       [ 5.33085935e-02],\n",
      "       [ 2.38915744e+00],\n",
      "       [-9.95014028e-01],\n",
      "       [-6.25830629e-02],\n",
      "       [ 1.63387958e-01],\n",
      "       [-1.60928337e+00],\n",
      "       [-6.60144750e-02],\n",
      "       [-7.79352260e-01],\n",
      "       [ 5.08157284e-01],\n",
      "       [-2.89864751e-01],\n",
      "       [ 1.41965925e+00],\n",
      "       [ 7.40116438e-01],\n",
      "       [ 1.15825550e+00],\n",
      "       [-7.18886869e-01],\n",
      "       [ 2.90733057e-01],\n",
      "       [ 1.09444162e-03],\n",
      "       [ 5.54492047e-01],\n",
      "       [-8.71517002e-01],\n",
      "       [-4.26304429e+00],\n",
      "       [-2.22546057e-01],\n",
      "       [ 7.48419368e-01],\n",
      "       [ 3.53380828e-01],\n",
      "       [ 2.37433517e-01],\n",
      "       [ 1.38436808e-01],\n",
      "       [-5.27142862e-01],\n",
      "       [ 3.87949533e-01],\n",
      "       [-2.69829257e+00],\n",
      "       [-9.59281160e-01],\n",
      "       [-5.68847753e-01],\n",
      "       [-1.34354681e+00]]), 'b': -0.32282985290520594}, {'dw': array([[ 0.        ],\n",
      "       [-0.01217772],\n",
      "       [-0.00287443],\n",
      "       [ 0.00341025],\n",
      "       [-0.00660725],\n",
      "       [-0.01468581],\n",
      "       [-0.0032896 ],\n",
      "       [-0.00977682],\n",
      "       [-0.01985833],\n",
      "       [ 0.02043909],\n",
      "       [-0.01624785],\n",
      "       [-0.03727284],\n",
      "       [ 0.01064143],\n",
      "       [ 0.01252924],\n",
      "       [ 0.01183861],\n",
      "       [-0.02334238],\n",
      "       [ 0.00935915],\n",
      "       [-0.01012407],\n",
      "       [-0.00274842],\n",
      "       [ 0.01055549],\n",
      "       [ 0.00890958],\n",
      "       [ 0.00667696],\n",
      "       [ 0.01290157],\n",
      "       [ 0.0026131 ],\n",
      "       [ 0.01298252],\n",
      "       [ 0.01861434],\n",
      "       [-0.00015619],\n",
      "       [-0.0088409 ],\n",
      "       [-0.0012246 ],\n",
      "       [-0.02332153],\n",
      "       [ 0.03269729],\n",
      "       [ 0.01797341],\n",
      "       [ 0.        ],\n",
      "       [ 0.02964666],\n",
      "       [-0.00301411],\n",
      "       [-0.0101275 ],\n",
      "       [ 0.02088046],\n",
      "       [ 0.00812487],\n",
      "       [-0.02416802],\n",
      "       [ 0.        ],\n",
      "       [ 0.00185852],\n",
      "       [ 0.00223835],\n",
      "       [-0.00541735],\n",
      "       [ 0.00632108],\n",
      "       [-0.03072666],\n",
      "       [-0.01384468],\n",
      "       [-0.00571051],\n",
      "       [-0.00566661],\n",
      "       [ 0.00176018],\n",
      "       [ 0.00249539],\n",
      "       [-0.00218313],\n",
      "       [ 0.00681787],\n",
      "       [ 0.02944777],\n",
      "       [ 0.00367488],\n",
      "       [ 0.00132548],\n",
      "       [ 0.00298035],\n",
      "       [ 0.00159998],\n",
      "       [-0.00293816],\n",
      "       [-0.00491164],\n",
      "       [ 0.01231417],\n",
      "       [ 0.00029027],\n",
      "       [ 0.00314121],\n",
      "       [ 0.01428182],\n",
      "       [ 0.01650226]]), 'db': 0.008553417624614632}, [array(30.22668014), array(10.36302233), array(8.77821943), array(7.97816053), array(7.47792665), array(7.13282367), array(6.87991514), array(6.68645966), array(6.53357636), array(6.4096072), array(6.30697995), array(6.22057223), array(6.14679813), array(6.08307186), array(6.02747999), array(5.97857404), array(5.93523545), array(5.89658518), array(5.86192163), array(5.83067721), array(5.80238719), array(5.77666699), array(5.75319533), array(5.73170157), array(5.71195599), array(5.69376228), array(5.67695166), array(5.66137821), array(5.64691514), array(5.63345176)])\n"
     ]
    }
   ],
   "source": [
    "print(optimize(w, b, X_train, y_train.reshape(-1, 1), 3000, learning_rate=1e-2, print_cost=\"True\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Exercise\n",
    "The previous function will output the learned w and b. We are able to use w and b to predict the labels for a dataset X. Implement the predict() function.    \n",
    "Two steps to finish this task:   \n",
    "1. Calculate $\\hat{Y} = A = \\sigma(w^T*X+b)$   \n",
    "2. Convert the entries of a into 0 (if activation <= 0.5) or 1 (if activation > 0.5), stores the predictions in a vector Y_prediction. If you wish, you can use an if/else statement in a for loop (though there is also a way to vectorize this)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(w, b, X, Y):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights\n",
    "    b -- bias \n",
    "    X -- data \n",
    "    Y -- label\n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    m = X.shape[1] # m:450\n",
    "    correct_num = 0\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0],1)\n",
    "    \n",
    "    A = sigmoid(np.dot(w.T, X) + b)\n",
    "    print(\"A\", A)\n",
    "    print(\"A.shape\", A.shape) # (1, 450)\n",
    "    for i in range(A.shape[1]):\n",
    "        if A[0][i] > 0.5:\n",
    "            Y_prediction[0][i] = 1\n",
    "    assert(Y_prediction.shape == (1,m))\n",
    "    \"\"\"\n",
    "    A.shape (1, 450)\n",
    "    correct_num: 409\n",
    "    precision: 0.9088888888888889\n",
    "    \"\"\"\n",
    "    for j in range(Y_prediction.shape[1]):\n",
    "        if Y_prediction[0][j] == Y.reshape(1, -1)[0][j]:\n",
    "            correct_num += 1\n",
    "    print(\"correct_num:\", correct_num)\n",
    "    print(\"precision:\", correct_num/m)\n",
    "    return Y_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 30.226680\n",
      "Cost after iteration 100: 10.363022\n",
      "Cost after iteration 200: 8.778219\n",
      "Cost after iteration 300: 7.978161\n",
      "Cost after iteration 400: 7.477927\n",
      "Cost after iteration 500: 7.132824\n",
      "Cost after iteration 600: 6.879915\n",
      "Cost after iteration 700: 6.686460\n",
      "Cost after iteration 800: 6.533576\n",
      "Cost after iteration 900: 6.409607\n",
      "Cost after iteration 1000: 6.306980\n",
      "Cost after iteration 1100: 6.220572\n",
      "Cost after iteration 1200: 6.146798\n",
      "Cost after iteration 1300: 6.083072\n",
      "Cost after iteration 1400: 6.027480\n",
      "Cost after iteration 1500: 5.978574\n",
      "Cost after iteration 1600: 5.935235\n",
      "Cost after iteration 1700: 5.896585\n",
      "Cost after iteration 1800: 5.861922\n",
      "Cost after iteration 1900: 5.830677\n"
     ]
    }
   ],
   "source": [
    "params, grads, costs = optimize(w, b, X_train, y_train.reshape(-1, 1), 2000, learning_rate=1e-2, print_cost=\"True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A [[1.00000000e+00 1.00000000e+00 5.61825855e-01 9.98034557e-01\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 9.98499006e-01\n",
      "  1.11621034e-19 1.29028446e-04 9.99999999e-01 8.05926452e-12\n",
      "  1.00000000e+00 9.99913856e-01 1.96709741e-16 9.99984667e-01\n",
      "  6.14460332e-07 1.49375274e-04 2.01370614e-09 1.00000000e+00\n",
      "  3.07431104e-21 9.99362035e-01 1.00000000e+00 1.00000000e+00\n",
      "  5.06336685e-15 3.58566521e-10 1.00000000e+00 1.70107762e-36\n",
      "  1.00000000e+00 1.00000000e+00 1.18942800e-28 9.99958041e-01\n",
      "  1.06814179e-39 4.56804477e-07 2.22264680e-22 7.22303445e-01\n",
      "  1.00000000e+00 9.99999999e-01 1.00000000e+00 1.79703691e-13\n",
      "  1.00000000e+00 3.02768089e-06 7.41635667e-24 3.55440289e-17\n",
      "  1.46653327e-15 1.00000000e+00 3.26089307e-25 4.08466660e-06\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 4.58225458e-25\n",
      "  7.04230510e-21 1.00000000e+00 6.27831062e-15 3.10335990e-04\n",
      "  6.19798576e-23 6.98808226e-03 1.00000000e+00 1.39550347e-23\n",
      "  1.00000000e+00 2.42633490e-18 1.14552935e-11 1.00000000e+00\n",
      "  1.94691490e-01 1.00000000e+00 9.81742572e-38 5.64275021e-39\n",
      "  7.07211791e-19 1.00000000e+00 9.66447641e-29 2.58758931e-09\n",
      "  1.85512347e-11 3.39424132e-14 1.91180429e-12 4.86172799e-08\n",
      "  9.58132104e-25 2.19539380e-11 7.71054922e-06 2.44389104e-36\n",
      "  8.48452630e-01 1.00000000e+00 9.99999538e-01 9.99999978e-01\n",
      "  1.00000000e+00 1.01086327e-09 1.00000000e+00 2.27697884e-28\n",
      "  1.00000000e+00 1.00000000e+00 4.50745491e-04 1.00000000e+00\n",
      "  9.99999957e-01 1.00000000e+00 2.34956461e-04 9.84514014e-01\n",
      "  2.49905120e-33 1.00000000e+00 9.99997635e-01 3.97772534e-07\n",
      "  1.00000000e+00 1.00000000e+00 2.23314069e-20 1.34252666e-20\n",
      "  1.00000000e+00 9.57959818e-01 1.55494694e-02 1.00000000e+00\n",
      "  5.39854299e-04 2.17396489e-32 9.71452643e-29 1.81393950e-24\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 7.02764217e-35\n",
      "  1.00000000e+00 1.00000000e+00 7.37887625e-46 2.92929408e-07\n",
      "  4.97742184e-05 1.00000000e+00 9.99996709e-01 2.22035307e-18\n",
      "  7.48860619e-24 1.00000000e+00 1.23455489e-07 9.34158290e-02\n",
      "  1.53949989e-07 4.15671712e-31 2.13824877e-11 9.99984386e-01\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 3.93076950e-31\n",
      "  1.71104108e-01 3.69187480e-19 9.99999983e-01 1.30737156e-28\n",
      "  1.00656045e-25 9.99991005e-01 1.00000000e+00 1.00000000e+00\n",
      "  1.00000000e+00 1.00000000e+00 3.55658109e-05 1.27176023e-12\n",
      "  3.16368588e-21 9.70848540e-04 1.00000000e+00 1.49434346e-05\n",
      "  1.00000000e+00 9.99967586e-01 4.83659410e-22 1.00000000e+00\n",
      "  1.69502982e-14 3.37827684e-08 1.00000000e+00 9.45594200e-17\n",
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      "  8.04102924e-01 9.99999490e-01 9.99936157e-01 1.00000000e+00\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
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      "  1.00000000e+00 4.20863928e-16 9.99999972e-01 9.99817170e-01\n",
      "  8.67995067e-42 1.35220321e-13 1.00000000e+00 1.00000000e+00\n",
      "  1.75630831e-12 1.00000000e+00 1.26456890e-24 5.28726070e-35\n",
      "  1.44258871e-02 8.37113282e-25 6.00861154e-06 2.40346861e-26\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 5.08910743e-22\n",
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      "  1.39853145e-20 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
      "  1.00000000e+00 3.24073993e-27 3.02454234e-03 1.00000000e+00\n",
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      "  9.99411137e-01 6.52640609e-05 1.00000000e+00 6.94212702e-09\n",
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      "  1.00000000e+00 9.99996978e-01 1.00000000e+00 1.00000000e+00\n",
      "  1.00000000e+00 6.90487449e-08 7.70567660e-38 1.02301628e-11\n",
      "  1.00000000e+00 8.22894841e-14 1.00000000e+00 2.63623492e-24\n",
      "  1.00000000e+00 1.01327645e-13 1.00000000e+00 8.11300678e-23\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 1.47309980e-08\n",
      "  9.99999994e-01 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
      "  2.21489786e-27 1.00000000e+00 1.00180057e-23 1.00000000e+00\n",
      "  4.02501177e-09 9.99995508e-01 2.75013834e-10 1.00000000e+00\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 4.01954451e-24\n",
      "  1.00000000e+00 9.99998503e-01 1.00000000e+00 9.99999916e-01\n",
      "  1.00000000e+00 1.45329313e-02 9.99999985e-01 5.33012693e-05\n",
      "  3.84659848e-18 1.00000000e+00 1.00000000e+00 3.20314240e-01\n",
      "  4.33043428e-11 3.13334050e-21 1.00000000e+00 2.84051048e-18\n",
      "  9.86487359e-01 6.62895187e-03 7.21614390e-14 9.34257384e-31\n",
      "  1.46898519e-25 1.00000000e+00 3.52529122e-12 9.99999905e-01\n",
      "  9.99999988e-01 1.00000000e+00 3.37984144e-19 1.00000000e+00\n",
      "  6.79273494e-12 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
      "  9.99999995e-01 1.00000000e+00 1.00000000e+00 4.13873256e-12\n",
      "  1.00000000e+00 1.00000000e+00 9.99986196e-01 1.00000000e+00\n",
      "  7.73742284e-01 1.00000000e+00 1.00000000e+00 2.19384423e-12\n",
      "  5.64754835e-18 1.00000000e+00 1.00000000e+00 1.00000000e+00\n",
      "  1.00000000e+00 1.00000000e+00 1.00000000e+00 3.69764001e-20\n",
      "  1.00000000e+00 7.90891980e-14 9.99999993e-01 6.60583350e-28\n",
      "  2.42474621e-19 2.72867314e-19 1.09034604e-02 1.00000000e+00\n",
      "  1.47761165e-22 2.82639857e-23 9.99999999e-01 6.18603253e-23\n",
      "  9.99999999e-01 1.00000000e+00]]\n",
      "A.shape (1, 450)\n",
      "correct_num: 409\n",
      "precision: 0.9088888888888889\n"
     ]
    },
    {
     "data": {
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       "        1., 1.]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(params['w'], params['b'], X_test.T, y_test.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5- Merge all functions into a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations !! You have finished all the necessary components for constructing a model. Now, Let's take the challenge to merge all the implemented function into one model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    '''\n",
    "    Compute the sigmoid of z\n",
    "    Arguments: z -- a scalar or numpy array of any size.\n",
    "    \n",
    "    Return:\n",
    "    s -- sigmoid(z)\n",
    "    '''\n",
    "    s = 1. / (1 + np.exp(-1 * z))    \n",
    "    return s\n",
    "\n",
    "# Random innitialize the parameters\n",
    "def initialize_parameters(dim):\n",
    "    '''\n",
    "    Argument: dim -- size of the w vector    \n",
    "    Returns:\n",
    "    w -- initialized vector of shape (dim,1)\n",
    "    b -- initializaed scalar\n",
    "    '''\n",
    "    \n",
    "    w = np.random.randn(dim, 1)\n",
    "    b = 0.0\n",
    "#     print(b)\n",
    "    # assert作用:用于判断一个表达式，在表达式条件为 false 的时候触发异常\n",
    "    assert(w.shape == (dim,1))\n",
    "    assert(isinstance(b,float) or isinstance(b,int))   \n",
    "    return w,b\n",
    "\n",
    "def propagate(w,b,X,Y):\n",
    "    '''\n",
    "    Implement the cost function and its gradient for the propagation\n",
    "    \n",
    "    Arguments:\n",
    "    w - weights\n",
    "    b - bias\n",
    "    X - data\n",
    "    Y - ground truth\n",
    "    '''\n",
    "#     X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)\n",
    "    X = np.nan_to_num(X / X.max(axis=0))\n",
    "#     print(\"X\", X)\n",
    "    m = X.shape[1] # 列数\n",
    "    A = sigmoid(np.dot(X, w) + b)\n",
    "#     print(\"A.shape\", A.shape) # A.shape (1347, 1)\n",
    "#     print(\"A\", A)\n",
    "#     print(Y.T.shape) # (1, 1347)\n",
    "#     cost = (-1 / m)* np.dot(np.nan_to_num(Y*np.log(A))+(1-Y)*np.log(1-A)) \n",
    "    cost = (-1.0 / m)* (np.dot(Y.T, np.log(A)) + np.dot(1-Y.T, np.log(1-A)))\n",
    "#     print(\"cost\", cost)\n",
    "    \n",
    "    dw = (1.0/m)*np.dot(X.T, (A-Y))\n",
    "    db = (1.0/m)*np.sum(A-Y)\n",
    "    \n",
    "    assert(dw.shape == w.shape)\n",
    "    assert(db.dtype == float)\n",
    "    # 利用squeeze（）函数将表示向量的数组转换为秩为1的数组\n",
    "    cost = np.squeeze(cost)\n",
    "    assert(cost.shape == ())\n",
    "    \n",
    "    grads = {'dw':dw,\n",
    "             'db':db}\n",
    "    return grads, cost\n",
    "\n",
    "def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost=False):\n",
    "    '''\n",
    "    This function optimize w and b by running a gradient descen algorithm\n",
    "    \n",
    "    Arguments:\n",
    "    w - weights\n",
    "    b - bias\n",
    "    X - data\n",
    "    Y - ground truth\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- True to print the loss every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    params - dictionary containing the weights w and bias b\n",
    "    grads -- dictionary containing the gradients of the weights and bias with respect to the cost function\n",
    "    costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve.\n",
    "    \n",
    "    '''\n",
    "    costs = []\n",
    "    accs = []\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "        \n",
    "        grads, cost = propagate(w, b, X, Y)\n",
    "        \n",
    "        dw = grads['dw']\n",
    "        db = grads['db']\n",
    "        \n",
    "         \n",
    "        w = w - learning_rate*dw\n",
    "        b = b - learning_rate*db\n",
    "        \n",
    "        if i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "#             accs.append(predict(w, b, X, Y))\n",
    "#             print(predict(w, b, X, Y))\n",
    "#             print(w.shape)\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "    \n",
    "    params = {\"w\":w,\n",
    "              \"b\":b}\n",
    "    \n",
    "    grads = {\"dw\":dw,\n",
    "             \"db\":db}\n",
    "    \n",
    "    return params, grads, costs, accs\n",
    "\n",
    "def predict(w, b, X, Y):\n",
    "    '''\n",
    "    Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b)\n",
    "    \n",
    "    Arguments:\n",
    "    w -- weights\n",
    "    b -- bias \n",
    "    X -- data \n",
    "    Y -- label\n",
    "    Returns:\n",
    "    Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X\n",
    "    '''\n",
    "    m = X.shape[1] # m:450\n",
    "    correct_num = 0\n",
    "    Y_prediction = np.zeros((1,m))\n",
    "    w = w.reshape(X.shape[0],1)  \n",
    "    A = sigmoid(np.dot(w.T, X) + b)\n",
    "    for i in range(A.shape[1]):\n",
    "        if A[0][i] > 0.5:\n",
    "            Y_prediction[0][i] = 1\n",
    "    assert(Y_prediction.shape == (1,m))\n",
    "    \"\"\"\n",
    "    A.shape (1, 450)\n",
    "    correct_num: 409\n",
    "    precision: 0.9088888888888889\n",
    "    \"\"\"\n",
    "    for j in range(Y_prediction.shape[1]):\n",
    "        if Y_prediction[0][j] == Y.reshape(1, -1)[0][j]:\n",
    "            correct_num += 1\n",
    "    accuracy = correct_num / m\n",
    "    return accuracy\n",
    "\n",
    "def model(dim =X_train.shape[1], X_train=X_train, Y_train=y_train, X_test=X_test, \n",
    "          Y_test=y_test, num_iterations=200000, learning_rate=1e-2,print_cost=True):\n",
    "    \"\"\"\n",
    "    Build the logistic regression model by calling all the functions you have implemented.\n",
    "    Arguments:\n",
    "    X_train - training set\n",
    "    Y_train - training label\n",
    "    X_test - test set\n",
    "    Y_test - test label\n",
    "    num_iteration - hyperparameter representing the number of iterations to optimize the parameters\n",
    "    learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize()\n",
    "    print_cost -- Set to true to print the cost every 100 iterations\n",
    "    \n",
    "    Returns:\n",
    "    d - dictionary should contain following information w,b,training_accuracy, test_accuracy,cost\n",
    "    eg: d = {\"w\":w,\n",
    "             \"b\":b,\n",
    "             \"training_accuracy\": traing_accuracy,\n",
    "             \"test_accuracy\":test_accuracy,\n",
    "             \"cost\":cost}\n",
    "    \"\"\"\n",
    "    # step1: 初始化参数\n",
    "    w, b = initialize_parameters(dim)    \n",
    "    \n",
    "    # step2:定义字典d,存放返回值\n",
    "    d = {}\n",
    "    \n",
    "    # step3:前向反向传播, 梯度更新\n",
    "#     params, grads, costs = optimize(w, b, X_train, y_train.reshape(-1, 1), \n",
    "#                                     num_iterations, learning_rate=1e-2, print_cost=\"True\")\n",
    "    \n",
    "    params, grads, costs, accs = optimize(w, b, X_train, y_train.reshape(-1, 1), \n",
    "                                    num_iterations, learning_rate=1e-2, print_cost=\"True\")\n",
    "        \n",
    "    # step4:预测\n",
    "#     train_accuravy = accs\n",
    "    train_accuracy = predict(params['w'], params['b'], X_train.T, y_train.T)    \n",
    "    test_accuracy = predict(params['w'], params['b'], X_test.T, y_test.T)\n",
    "    \n",
    "    d = {\n",
    "        \"w\":w,\n",
    "        \"b\":b,\n",
    "        \"training_accuracy\": train_accuracy,\n",
    "        \"test_accuracy\":test_accuracy,\n",
    "        \"cost\":costs\n",
    "    }\n",
    "    \n",
    "    if print_cost==True:\n",
    "        print(\"training_accuracy:\", train_accuracy)\n",
    "        print(\"test_accuracy\", test_accuracy)\n",
    "        print(\"cost:\", costs[-1])\n",
    "        \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/Users/stone/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:40: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 40.300994\n",
      "Cost after iteration 1000: 6.343909\n",
      "Cost after iteration 2000: 5.796997\n",
      "Cost after iteration 3000: 5.602259\n",
      "Cost after iteration 4000: 5.506487\n",
      "Cost after iteration 5000: 5.450187\n",
      "Cost after iteration 6000: 5.413019\n",
      "Cost after iteration 7000: 5.386388\n",
      "Cost after iteration 8000: 5.366118\n",
      "Cost after iteration 9000: 5.349968\n",
      "Cost after iteration 10000: 5.336642\n",
      "Cost after iteration 11000: 5.325348\n",
      "Cost after iteration 12000: 5.315577\n",
      "Cost after iteration 13000: 5.306990\n",
      "Cost after iteration 14000: 5.299355\n",
      "Cost after iteration 15000: 5.292507\n",
      "Cost after iteration 16000: 5.286327\n",
      "Cost after iteration 17000: 5.280726\n",
      "Cost after iteration 18000: 5.275635\n",
      "Cost after iteration 19000: 5.270999\n",
      "Cost after iteration 20000: 5.266772\n",
      "Cost after iteration 21000: 5.262916\n",
      "Cost after iteration 22000: 5.259395\n",
      "Cost after iteration 23000: 5.256178\n",
      "Cost after iteration 24000: 5.253237\n",
      "Cost after iteration 25000: 5.250545\n",
      "Cost after iteration 26000: 5.248079\n",
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      "training_accuracy: 0.8930957683741648\n",
      "test_accuracy 0.8955555555555555\n",
      "cost: 5.203343933281586\n"
     ]
    }
   ],
   "source": [
    "d = model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.选做题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations on building your first logistic regression model. It is your time to analyze it further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.1 Observe the effect of learning rate on the leraning process.   \n",
    "Hits: plot the learning curve with different learning rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4.2 Observe the effect of iteration_num on the test accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Challenge ! ! !\n",
    "\n",
    "The original data have images labeled 0,1,2,3,4,5,6,7,8,9. In our logistic model, we only detect if the digit in the image is larger or smaller than 5. Now, Let's go for a more challenging problem. Try to use softmax function to build a model to recognize which digit (0,1,2,3,4,5,6,7,8,9) is in the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Congratulations ! You have completed assigment 4. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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